Data Engineer - Finance Government Programs; Hybrid

Resp & Qualifications

PURPOSE:

The Data Engineer, working within the Finance Data Systems and Decision Support area, is a highly motivated professional responsible for developing innovative and complex data solutions and applications to support actuarial analytics specializing in medical economics. With a critical eye towards data integrity and a growing understanding of claim and provider data, the Data Engineer will work closely with actuaries to produce trend analytics tools and dashboards and assist with ad hoc reporting and analysis.

Additionally, the Data Engineer will partner with actuaries to support the financial impact analysis related to the building of and changes to Government Program analytics.

The Data Engineer is expected to have a basic understanding of the health care business and is strongly encouraged to quickly improve on this business knowledge to perform analysis more independently on the data to validate accuracy and proper reporting based on established business rules.

The Data Engineer is expected to make decisions, with management guidance, on the appropriate data sources to use, the optimal approach to solving business problems, and to ensure that results tie to appropriate controls.

The Data Engineer is expected to be proficient in SQL. Experience in Microsoft business intelligence tools is beneficial, specifically knowledge and experience of SSIS (SQL Server Integration Services) and SSAS (SQL Server Analysis Services). With the goal of moving to cloud-based tools, practical experience with Azure Data Factory is preferred along with IICS (Informatica Intelligent Cloud Services) and Snowflake.

The Data Engineer is expected to learn and understand the fundamentals of actuarial and financial analytics. This includes becoming familiar with actuarial terminology and basic calculations as well as with the underlying health care data being used for analysis to be able to explain reasons for outliers and to generally support in-depth questions and analytical results.

The Data Engineer will be responsible and accountable for all phases of the System Development Life Cycle. This requires an understanding of business need, the data needed to respond to the request, and the ability to execute on the building and delivery of accurate and timely reporting and data solutions.

This is a fast-paced, collaborative, and iterative environment requiring quick learning, agility, and flexibility.

• Develops and enhances data models and solutions to enable business partners to build analytics framework and make data-driven business decisions. Works as the liaison in translating the business rules and supporting data needs from infrastructure systems (e.g., data warehouses, data lakes). Prepares and manipulates data using multiple technologies.
• Interprets data, analyzes patterns using various data quality check techniques, and provides ongoing reports. Executes quantitative analyses that translate data into actionable insights. Provides analytical and data-driven decision-making support for key projects. Designs, manages, and conducts quality control procedures for data sets using data from multiple systems.
• Develops data models by studying existing data warehouse architecture; evaluating alternative logical data models including planning and execution tables; applying metadata and modeling standards, guidelines, conventions, and procedures; planning data classes and sub-classes, indexes, directories, repositories, messages, sharing, replication, back-up, retention, and recovery.
• Support actuaries' data needs via ad hoc requests that may involve exploring new data sets.
• Improves data delivery engineering job knowledge by attending educational workshops; reviewing professional publications; establishing personal networks; benchmarking state-of-the-art practices; participating in professional societies.
• Applies data extraction, transformation and loading techniques in order to connect large data sets from a variety of sources.
• Applies and implements best practices for data auditing, scalability, reliability and application performance.

This position has no direct reports, however, may informally lead teams in a matrix environment.

Education Level: Bachelor's Degree in Information Technology or Computer Science or Engineering OR in lieu of a Bachelor's degree, an additional 4 years of relevant work experience is required in addition to the required work experience.

Experience:

3 years hands‑on experience with database management systems, ELT/ETL systems, business intelligence tools, defect management and system testing.

• Existing knowledge and experience working with health care claims and enrollment data in the health insurance industry is highly preferred. Basic and fundamental understanding of health economics and advanced analytics methodology is desired for this role.

• Knowledge and understanding of at…